advanced~3h
pgvector — Vector Similarity Search Inside PostgreSQL
The storage and indexing mechanics behind pgvector — PostgreSQL's extension for storing embeddings and running similarity search directly in the database — and why 'just use Postgres' has become a credible alternative to standing up a dedicated vector database for many real workloads.
Learning objectives
- Store embedding vectors in a PostgreSQL column using the pgvector extension's vector type.
- Choose between the L2, cosine, and inner-product distance operators based on how a given embedding model was trained.
- Explain the difference between exact nearest-neighbor search and approximate nearest-neighbor search, and why ANN indexes trade recall for speed.
- Build and tune an HNSW or IVFFlat index for approximate nearest-neighbor search at scale.
- Articulate the operational case for choosing pgvector over a dedicated vector database at moderate scale, and recognize when that trade-off stops holding.
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pgvector — Vector Similarity Search Inside PostgreSQL